The motivation of Residual Networks is that very deep networks are so good at fitting complex functions that when training them we almost always overfit the training data. True/False?

Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.

Which ones of the following statements on Residual Networks are true? (Check all that apply.)

Which is false?Group of answer choicesOutput of a layer in the residual network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of a layer in the dense network is the normal output (i.e, what’s produced after applying a filter and an activation function) + the layers input.Output of layer 𝑙 in the dense network will be one of the inputs for layers 𝑖∈(𝑙+1,𝐿) where 𝐿 is the total number of layers.A problem of convolution network is that some features may get extracted earlier in the network, but could be useful later on. However, it is hard to keep track of previous non-modified outputs.

Deep learning is a subset of machine learning algorithms that uses multiple layers to progressively extract information from the raw input to give better output.Select one:a. Trueb. False

What is the primary purpose of using a residual plot in regression analysis?To visualize the correlation coefficient.To check for homoscedasticity and linearity.To determine the slope of the regression line.To classify data points